In vivo medical imaging is becoming an indispensable tool in the screening, diagnosis, staging, treatment, and monitoring of cancer and a host of other diseases. However, the full potential of these technologies has been severely limited by image processing time and memory management of large data sets. In order to address this problem, Diagnostic Photonics, Inc. proposes to develop and distribute a data segmentation/ desegmentation method and corresponding algorithms for the reconstruction of images from large multiplexed data sets for optimized speed and memory usage. Utilizing principles of physics in conjunction with computer science, the algorithms will be able to produce faster and better images compared to their competition which includes improved computer hardware and software compilers. These algorithms can be implemented across a wide array of medical imaging modalities including synthetic aperture techniques and other imaging modes in ultrasound, MRI, optical quantum dot imaging and optical coherence tomography among others. Beyond medicine, the utility of the method extends to defense applications such as synthetic aperture radar.